AlexNet [1]} is the name of a convolutional neural network, which competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. As mentioned in [1]}, AlexNet consists of five convolutional layers and three fully-connected layers with a final 1000-way softmax layer and has 60 million parameters and 650,000 neurons. We compare two neural networks AlexNet1 [3]} and AlexNet2 [4]} implemented in Caffe. These two neural networks have the same structure and close accuracy but different initial parameters and were trained independently. As a metaphor, they are twins who live in the same environment and have the same education, yet they could have different learning skills and acquire different knowledge in certain aspects. The work in this paper can be easily generalized to the comparison of any two neural networks with the same inputs. In contrast to existing approaches like [5]}, our framework focus on studying the intrinsic geometry of manifolds, namely, curvature information.
